An integrated system theoretic accident model and process (STAMP)-Bayesian network (BN) for safety analysis of water mist system on tanker ships

Abstract

A water mist on tanker ships is a specialized fire suppression system intended to mitigate fire hazards on board. This innovative safety measure disperses microscopic water droplets, creating a mist that rapidly absorbs heat and suppresses fires by reducing oxygen levels and cooling the surrounding area autonomously. This paper attempts to perform a comprehensive safety analysis of the water mist system on a tanker ship under the system theoretic accident model and process (STAMP) and Bayesian network (BN) robust modeling, which is capable of presenting a dynamic structure comprising complex elements. In the paper, whilst the STAMP is utilized to determine failure scenarios by establishing a hierarchical control and feedback model structure, the BN quantifies the probabilities of potential failures based on the outcomes of STAMP. A water mist system in tanker ships is analyzed under STAMP-BN modeling since it is an essential part of the ship, including an advanced system comprising elements of human, software, and hardware. The research results indicate that the failure probability of the water mist system has a failure probability of 3.93E-02. Besides its robust theoretical background, the research findings will provide a helpful reference for maritime safety managers, safety inspectors, technical inspectors, HSEQ managers, and ship crew on improving fire-fighting safety in tanker ships.